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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2022, Vol. 43 ›› Issue (5): 125137.doi: 10.7527/S1000-6893.2021.25137

• Fluid Mechanics and Flight Mechanics • Previous Articles     Next Articles

Quantitative evaluation of flight risk of icing aircraft based on theory of region of attraction and binary extreme value

WU Qiang1, XU Haojun1, PEI Binbin1, WEI Yang1, ZHANG Yang2   

  1. 1. Aeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China;
    2. 95633 Troops, Chinese People's Liberation Army, Chengdu 611530, China
  • Received:2020-12-22 Revised:2021-01-13 Published:2021-03-01
  • Supported by:
    National Natural Science Foundation of China (61873351)

Abstract: Icing will cause the flight envelope of the aircraft to shrink, and flight accidents are prone to occur if the pilot does not handle it properly. To quantitatively calculate the aircraft flight risk under icing conditions and formulate a reasonable icing risk avoidance strategy, the aircraft longitudinal dynamics system is analyzed. The two-dimensional region of attraction of the aircraft after icing is calculated based on the region of attraction method, and the flight state beyond the region of attraction is used as the judging criterion for flight accident. A typical man-machine-loop system model is established. The extreme value samples of key flight safety parameters are extracted through Monte Carlo simulation. According to the extreme value theory, a binary extreme value Copula model is established. The model parameters are identified by the genetic algorithm. A goodness-of-fit test method is used to determine the optimal distribution model, and then the flight risk probability under the conditions of different icing influence levels is calculated, which can provide guidance for the pilot to maneuver to ensure flight safety. The method proposed provides a new idea for quantitatively calculating the flight risk probability under icing conditions, and has good application prospects.

Key words: flight risk, region of attraction, Monte Carlo simulation, extreme value theory, binary extreme value Copula model

CLC Number: